In order to improve the quality of systematic researches, various tools have been developed by well-known scientific institutes sporadically. Dr. Nader Ale Ebrahim has collected these sporadic tools under one roof in a collection named “Research Tool Box”. The toolbox contains over 720 tools so far, classified in 4 main categories: Literature-review, Writing a paper, Targeting suitable journals, as well as Enhancing visibility and impact factor.

By their very nature, citation cartels are difficult to detect.
Unlike self-citation, which can be spotted when there are high levels of
references to other papers published in the same journal, cartels work
by influencing incoming citations from other journals.

This year, Thomson Reuters suspended Applied Clinical Informatics (ACI) for its role in distorting the citation performance of Methods of Information in Medicine (MIM). Both journals are published by Schattauer Publishers in Germany. According to the notice, 39% of 2015 citations to MIM came from ACI.
More importantly, 86% of these citations were directed to the previous
two years of publication — the years that count toward the journal’s
Impact Factor.

Thomson Reuters purposefully avoids using the term “citation cartel,”
which implies a willful attempt to game the system, and uses the more
ambiguous term “citation stacking” to describe the pattern itself.
Ultimately, we never know the intent of the authors who created the
citation pattern in the first place, only that it can distort the
ranking of a journal within its field. This is what Thomson Reuters
wants to avoid.

Schattauer Publishers appealed the suspension,
offering to exclude the offending papers from their Impact Factor
calculation as a concession. Their appeal was denied. Offering some
consolation to its readers, the publisher made all 2015 ACI papers freely available. It has also offered all ACI authors one free open access publication in 2016.

To better understand the citation pattern that resulted in ACI being suspended, I created, using VOS Viewer, a visualization of the citation network of papers published in ACI (blue) and MIM (red) from 2013 through 2015. Each paper lists its first author, year of publication and links to the papers it cites.

Citation
network of papers published in Applied Clinical Informatics (blue) and
Methods of Information in Medicine (red), 2013–2015.

From the graph, there appear to be four papers that strongly influence the flow of citations in this network, two MIM papers by Lehmann (red) and two ACI
papers by Haux (blue). Each of these papers cites a large number of
papers published in the other journal within the previous two
years. Does this alone imply an intent to distort one’s Impact Factors?
We need more information.

Both Lehmann and Haux are on the editorial boards of both journals. Lehman is the Editor-in-Chief of ACI and also sits on the editorial board of MIM. Haux is the Senior Consulting Editor of MIM and also sits on the ACI editorial board.
This illustrates that there is a close relationship among the two
editors, but still this is not enough to imply intent. We need to look
at the four offending papers:

The 2014 Lehmann paper (coauthored
by Haux) includes the following methods statement in its abstract:
“Retrospective, prolective observational study on recent publications ofACI and MIM. All publications of the years 2012 and 2013 from these journals were indexed and analysed.”

Similarly, the 2014 Haux paper (coauthored
by Lehmann) includes this methods statement: “Retrospective, prolective
observational study on recent publications of ACI and MIM. All publications of the years 2012 and 2013 were indexed and analyzed.”

The 2015 Lehman paper states: “We conducted a retrospective observational study and reviewed all articles published in ACI during the calendar year 2014 (Volume 5)…”, and lastly,

What is similar among these four papers written by ACI and MIM
editors is that they are analyzing papers published in their own
journals within the time frame that affects the calculation of their
Impact Factors. Again, this alone does not imply an intent to game their
Impact Factor. Indeed, the publisher explained that
citation stacking was “an unintentional consequence of efforts to
analyze the effects of bridging between theory and practice.”

I can’t dispute what the editors and publisher state was their
intent. However, what is uniformly odd about these papers is that they
cite their dataset as if each datapoint (paper) required a reference.

Why is this odd? If I conducted a brief analysis and summary of
all papers published in a journal, would I need to cite each paper
individually, or merely state in the methods section that my dataset
consists of all 70 research papers published in Journal A in years X
and Y? While ACI and MIM are relatively small journals, if this approach were used to analyze papers published in, say, PNAS, their reference section would top 8000+ citations. Similarly, a meta-analysis of publication in PLOS ONE
would require citing nearly 60K papers. Clearly, there is something
about the context of paper-as-datapoint that distinguishes it from
paper-as-reference.

One could play devil’s advocate by assuming that it is normal
referencing behavior in the field of medical informatics to cite one’s
data points, even if they are papers, and unfortunately we’ve seen this
pattern before. In 2012, I took the editor of another medical
informatics journal to task for a similar self-referencing study. The editor conceded by removing all data points from his reference list, acknowledging that this was a “minor error” in a correction statement.
Citing papers-as-datapoints, in the cases of Lehmann and Haux is not
standard citation practice. The editors should have known this.

If it was not the intention of the editors to influence their
citation performance, there were other options open to them at the time
of authorship:

They could have simply described their dataset without citing each paper.

If citing each paper was important to the context of their paper,
they could have worked from a group of papers published outside the
Impact Factor window. Or,

They could have listed their papers in a footnote, appendix, or provided simple online links instead of formal references.

Suspension from receiving a Journal Impact Factor can be a serious
blow to the ability of a journal to attract future manuscripts. The
editors apologized for their actions in an editorial published soon after ACI suspension. In the future, they will refrain from publishing these kinds of papers or put their references in an appendix.

The measurement of scientific progress remains a significant
challenge exasperated by the use of multiple different types of metrics
that are often incorrectly used, overused, or even explicitly abused.
Several metrics such as h-index or journal impact factor (JIF) are often
used as a means to assess whether an author, article, or journal
creates an "impact" on science. Unfortunately, external forces can be
used to manipulate these metrics thereby diluting the value of their
intended, original purpose. This work highlights these issues and the
need to more clearly define "impact" as well as emphasize the need for
better metrics that leverage full content analysis of publications.

Visibility and Citation Impact

Abstract

The number of publications is the first criteria for assessing
a researcher output. However, the main measurement for author
productivity is the number of citations, and citations are typically
related to the paper's visibility. In this paper, the relationship
between article visibility and the number of citations is investigated. A
case study of two researchers who are using publication marketing tools
confirmed that the article visibility will greatly improve the citation
impact. Some strategies to make the publications available to a larger
audience have been presented at the end of this paper.

Si Niu, X. (2014). International scientific collaboration
between Australia and China: A mixed-methodology for investigating the
social processes and its implications for national innovation systems.
Technological Forecasting and Social Change, 85: 58-68.
http://dx.doi.org/10.1016/j.techfore.2013.10.014

Vieira, E., Cabral, J. & Gomes, J. (2014). How good is a
model based on bibliometric indicators in predicting the final decisions
made by peers? Journal of Informetrics, 8(2): 390-405.
http://dx.doi.org/10.1016/j.joi.2014.01.012

Abstract: Earlier publications
have shown that the number of references as well as the number of
received citations are field-dependent. Consequently, a long reference
list may lead to more citations. The purpose of this article is to study
the concrete relationship between number of references and citation
counts. This article tries to find an answer for the concrete case of
Malaysian highly cited papers and Malaysian review papers. Malaysian
paper is a paper with at least one Malaysian affilation. A total of 2466
papers consisting of two sets, namely 1966 review papers and 500
highly-cited articles, are studied. The statistical analysis shows that
an increase in the number of references leads to a slight increase in
the number of citations. Yet, this increase is not statistically
significant. Therefore, a researcher should not try to increase the
number of received citations by artificially increasing the number of
references.

1. Introduction

Researchers seeking citation tracking to find the
most influential articles for a particular topic and to see how often
their own published papers are cited (Bakkalbasi et al. 2006). On the other hand universities are looking for citations because of its influence in the university ranking (Ale Ebrahim et al. 2013, Ioannidis 2010, Bornmann, Leydesdorff, and Wang 2014).
A citation count is the number of times a research work such as a
journal article is cited by other works. The citation per paper
meaningfully influence a number of metrics, including total citation
counts, citation speed, the ratio of external to internal cites,
diffusion scores and h-index (Carley, Porter, and Youtie 2013). Citation counts still commonly use for the measure of research papers quality and reputation (Abt and Garfield 2002). The number of citations that an article receives measured its impact on a specific field (Lai, Darius, and Lerut 2012). Citation analysis is one of the most important tools to evaluate research performance (Bornmann et al. 2012). Citation indicator is important for scientists and universities in all over the world (Farhadi, Salehi, Yunus, et al. 2013).
In the early stage, the relationship between the number of references
and the number of the paper citation was investigated in the 1965 (UZUN 2006, de Solla Price 1965). A long reference list at the end of a research paper may be the key to ensuring that it is well cited (Corbyn 2010, Ball 2008). Hence, citation counts are correlated with reference frequencies (Abt and Garfield 2002). Webster, Jonason, and Schember (2009)
raised the question “Does the number of references an article contains
predict its citation count?” and found that reference counts explained
19% of the variance in the citation counts. Lancho-Barrantes, Guerrero-Bote, and Moya-Anegón (2010)
found that not only the number, but also the citation impact of the
cited references correlated with the citation counts for a paper. The
higher the impact of the cited references, the higher the later impact
of the citing paper (Bornmann et al. 2012). Review articles are usually highly cited compare to other types of papers (Meho 2007).

2.Materials and methods

All data were obtained through Web of Science
online academic database provided by Thomson Scientific. This database
included the necessary information to examine the relationship between
reference and citation counts for every review and highly cited papers
published in Malaysia since 1980 to October 2013. Science Citation Index
Expanded, Social Sciences Citation Index and Arts & Humanities
Citation Index, were searched for reviews and highly cited papers. For
each paper, all Bibliometrics data, especially the number of references
and the number of times the paper has been cited during the interval
between the year of publication and the year 2013, have been
collected.Two samples set were selected: 1- The sample number one
consisted of 1966 review papers in all disciplines from Malaysia,
according to the Web of Knowledge’s classification system. Citation
statistics produced by shorter than three years’ time frame may not be
sufficiently stable (Adams 2005, UZUN 2006).
Because, papers appearing in the Web of Science databases over the last
few years, have not had enough time to accumulate a stable number of
citations (Webster, Jonason, and Schember 2009).
Therefore, the time span limited from 1980 to November, 2010; yielding a
subsample of 721 publications (37% of the original sample).
Publications with zero citation were removed. In order to select the
highly cited paper a threshold 10 times cited per year is considered.
The association between the number of references (independent variable)
and time cited per year (dependent variable) of highly cited review
papers investigated with linear and non-linear models. 2- The sample
number two comprises 500 highly cited publications from Malaysia.
According to the Web Of Science classification, the results are obtained
based on the article type and exclude the review articles, editorial
material, conference papers and book review.

3. Results and discussion

Two sets of data 1- 1966 review papers and 2- 500
high cited papers, were investigated separately. The results and
discussions are coming as follows.

Outliers for sample one (1966 review papers)

Due to the effect of the age of an article, the number of citations
cannot be a reference of highly cited paper. Therefore, the citation per
year selected as a reference for highly cited paper. Papers with 10
times cited per year is considered as highly cited paper. Figure 3-1
shows the number of times cited per year for 660 review papers. A
threshold was visually determined on 50 times cited per year. Papers
with more than 50 times cited yearly is called “extremely high cited
paper” and detected as outliers. Papers with more than 300 listed
references also detected as outliers (3-2).

Figure 3-1 Number of times cited per year vs number of review papers references

Figure 3-2 Number of times cited per year vs number of references in review paper

Correlation analysis for sample one (1966 review papers)

The correlation between variables was modeled with regression model, linear model

y = α x + β and exponential model, non-linear model y = α eβx. The
goodness of both model was then measured with Spearman’s rho , Kendall’s
tau and Pearson correlation coefficient . The result of correlation
analysis is summarized in 3-1.

The association between variables is
graphically illustrated with scatter plots. The trend of these
associations was drawn with solid lines. Refer to Figure 3 and Figure 4,
both linear and non-linear models are not significantly fitted, trends
are positive which support the hypothesis “For a given review paper,
increasing in the number of references may have result of increasing the
times cited per year”.

Table 3-1 The result of correlation analysis of highly-cited review papersFigure 3-3 Relationship between number of references andcitation counts in review papers (linear model)Figure 3-4 Relationship between number ofreferences and citation counts in review papers (Exponential model)

Outlier detection for sample two (500 highly cited papers)

Papers with 10 times cited per year is considered as highly cited
paper. Papers that cited more than 100 times per year is considered as
extremely high cited paper and detected as an outlier. Figure 5 and
Figure 6 are showing raw data and filtered data respectively.

Figure 3-5 Raw data – Number of times cited per year vs number of references 500 highly cited papersFigure 3-6 Filtered data – Number of times citedper year vs number of references in 500 highly cited papers

Correlation analysis for sample two (500 highly cited papers)

The association between the number of
references (independent variable) and time cited per year (dependent
variable) of first 500 high cited papers investigated with linear and
non-linear model correlation analysis. The correlation was modeled with
regression model, linear model y = α x + β and exponential model,
non-linear model y = α eβx. The goodness of fit was then measured with
Spearman’s rho , Kendall’s tau and Pearson correlation coefficient . The
result of correlation analysis is summarized in Table 3-2.

Table 3-2 The result of correlation analysis of 500 highly cited papers.

The association between variables is
graphically illustrated with scatter plots. The trend of these
associations is shown by the solid lines. Figure 3-7 and Figure 3-8
shows, although both linear and non-linear models are not significantly
fitted, positive values of correlation coefficients are still suggesting
a positive trend (positive correlation) on the number of references and
the number of times cited per year.

Figure 3-7 Relationship between number of references and citation counts in 500 highly cited (linear model)Figure 3-8 Relationship between number of referencesand citation counts in 500 highly cited (Exponential Model)

4. Conclusion

This study shows that since the trend
between the citation count and the number of references is not
statistically significant, we cannot conclude that there is a
significant association between the citation count of Malaysia review
papers between the given period and number of references contained in
the paper. The correlation coefficient is not statistically significant.
However, r = 0.152 based on the population of 721 articles. Malaysian
review papers get more citations than other types of papers. The number
of references in the article has the lowest impact on the citation
compares with review paper. As this study looked only Malaysia review
papers and 500 highly-cited article, it would be necessary to conduct a
similar study in the otherworld and types of papers. It would be
important to examine whether in other types of papers the relationship
investigated here have significant correlated or not. The research
considered the general definition of citations. Therefore, future
studies may make a diffrentianain between “perfunctory citations” and
“organic citations” citations as Tang and Safer (2008)
defined “perfunctory citations” is occurred only once and in the
introduction, “organic citations” as references cited for “conceptual
ideas” and “methodology and data” reasons.ACKNOWLEDGEMENTSincere
thanks to Dr. Bojan Obrenović and the International Journal of
Management Science and Business Administration’s board members for their
useful advices. References

Abt, Helmut A., and Eugene Garfield. 2002. “Is the relationship
between numbers of references and paper lengths the same for all
sciences?” Journal of the American Society for Information Science and
Technology 53 (13):1106-1112. doi: 10.1002/asi.10151.

Search This Blog

About Me

Nader Ale Ebrahim has
a Technology Management PhD degree from the Department of Engineering
Design and Manufacture, Faculty of Engineering, University of Malaya
(UM), Kuala Lumpur, Malaysia. He holds a Master of Science in the
mechanical engineering from University of Tehran, Iran.